Paper to be presented at the DRUID Academy conference in Rebild, Aalborg, Denmark on January
15-17, 2014
Entrepreneurial Cognition when faced with Risk and Uncertainty:
Methodological Reflections
Giulio Zichella
Copenhagen Business School
INO
[email protected]
Abstract
The purpose of the paper is to review and suggest a categorization of methodologies used to trigger entrepreneurial
behavior in a variety of settings involving risk and uncertainty. Even though the link between entrepreneurship and risk is
undeniably tight, literature often suffers from low predictive power when explaining such link empirically. We argue that
this might be due a lack of consensus over both the definition and operationalization of the constructs of risk and
uncertainty in entrepreneurship, with particular reference to risk perception. Three types of methodological designs are
identified and presented being money games, scenario-based questionnaires, and hybrids. By looking at their
advantages and disadvantages, we aim at contributing to a better understanding of the most suitable game designs to
capture risk perception, suggesting their use in specific settings and ultimately helping future research in
entrepreneurship.
Jelcodes:M21,M21
AT YOUR OWN RI“K - ENTREPRENEURIAL BEHAVIOR WHEN FACED WITH RISK
AND UNCERTAINTY: METHODOLOGICAL REFLECTIONS
Giulio Zichella Copenhagen Business School, [email protected] (work in progress: please do not cite)
Abstract
The purpose of the paper is to review and suggest a categorization of methodologies used to trigger
entrepreneurial behavior in a variety of settings involving risk and uncertainty. Even though the link
between entrepreneurship and risk is undeniably tight, literature often suffers from low predictive power
when explaining such link empirically. We argue that this might be due a lack of consensus over both the
definition and operationalization of the constructs of risk and uncertainty in entrepreneurship, with
particular reference to risk perception. Three types of methodological designs are identified and presented
being money games, scenario-based questionnaires, and hybrids. By looking at their advantages and
disadvantages, we aim at contributing to a better understanding of the most suitable game designs to
capture risk perception, suggesting their use in specific settings and ultimately helping future research in
entrepreneurship.
Key words: Entrepreneurial behavior, Risk, Uncertainty, Risk perception, Money Games, Hybrids
1. INTRO
Risk and uncertainty are consistently viewed by scholars as two highly influential variables in
entrepreneurship, especially at the individual level (Miller, 1983; Zhao Et Al, 2010). In particular,
risk perception has been one of the main investigation candidates in recent years to explain
differences
between entrepreneurs and non-entrepreneurs
(Simon Et Al, 2000; Keh Et Al, 2002). Risk perception is defined as the subjective judgment of risk
inherent in a situation (Cooper Et al, 1988; Smith, 2003; Keh Et Al, 2002). Measuring the impact of
risk perception is still an open problem as a lack of consensus between scholars exists on best
measurement practices (Stewart & Roth, 2001). All these elements have fostered the need for
filling a research gap in entrepreneurship literature, that is to map games designed to stimulate
risk perception (Baron & Ensley, 2006). Our research question then
Which games are best
suited to capture entrepreneurial risk perception?
Entrepreneurship provides a unique background to study risk perception due to the high amount
of subjectivity in
decision making (Cooper Et. Al., 1988; Barney & Busenitz, 1997).
A
of an economic venture, especially the one who organizes,
B
In becoming an
entrepreneur an individual risks, among other things, career opportunities, family relations,
financial and psychic well-being (Liles, 1974; Bird, 1989). A better understanding of risk perception
role in new venture formation is essential given the significant failure rate among new ventures.
Furthermore, it has the potential to improve the quality of decision making in the risk-charged
environments which most prospective founders of new firms face (Forlani & Mullins, 2000).
1
We build our arguments on the idea that individuals decisions in risky contexts are based on
cognitive elements such as perceptions and attitudes towards risk. In general, humans are usually
far from being rational due to a variety of factors that deal with cognition; among others, we can
identify overconfidence, information overload and counterfactual thinking. These cognitive biases
ultimately affect behavior through the mediating effect of risk perception. (Kahneman & Lovallo,
1993; Simon Et Al, 2000; Baron, 2000). The presence of different biases in decision making has
strong implications in entrepreneurship. Some scholars believe that without the use of heuristics
in decision making, business opportunities discovery and implementation would be much more
limited (Busenitz & Barney, 1997). This fact, in turn, creates a challenge: how can we account for
these cognitive biases when assessing entrepreneurial risk perception? (Baron & Ward, 2004).
For decades, researchers have used two types of games to measure the impact of cognitive bias:
money based and scenario based games. Money based games focus on measuring behavior on
tasks (e.g. gambles) including real monetary stakes. Scenario based games focus on measuring
complexity in decision making through the use of scenarios. To date there is not a paper that
reviews the use of these games in entrepreneurship literature and this is where we aim at
contributing. We suggest that the decision to use a particular game to measure risk perception has
to be driven by the research objectives. We argue that a hybrid design between money and
scenario based games might be the most beneficial for entrepreneurship research.
The paper is structured as follows: in section two we briefly review conceptually the two main
variables of risk and uncertainty, underlining the role of risk perception in uncertain situations. In
section three we review two different types of game design and present their general features,
structures, and advantages versus disadvantages. In section four we present a third category of
games, a hybrid type, arguing for its importance in the context of entrepreneurship. Section four
concludes.
2. RISK AND UNCERTAINTY: A BRIEF CONCEPTUAL REVIEW
Risky choices and the selection criteria that people seek in order to optimize decision making has
been the object of theoretical and empirical investigation for centuries (Machina 1987 ; Glimcher,
2003); this creates the conditions to review some of the most widely recognized definitions and
operationalization of risk and uncertainty and how overtime the role of risk perception has
become central at the individual level of analysis.
2.1 Definitions
There is a sharp conceptual distinction between decisions under risk and under uncertainty and
that depends on the different availability of information possessed by the decision maker (Knight,
2
1921). Definition wise, risk refers to situations where individuals know with certainty the
mathematical probabilities of possible outcomes of choice alternatives. Uncertainty refers to
situations where the likelihood of different outcomes cannot be expressed in a mathematical
probability form. Two types of uncertainty can be mentioned: aleatory and epistemic uncertainty.
Aleatory uncertainty is the objective and irreducible uncertainty about events whereas epistemic
uncertainty is the subjective and reducible uncertainty that results from a lack of knowledge
related to the events (Weber & Johnson, 2008). A classic economic assumption is that every
uncertain situation can be reduced to a risky one by assuming all the possible outcomes as equally
likely. However, research showed that people
distinguishing
between risky and uncertain options and having a preference for risk (Ellsberg, 1961). In real life
decisions, uncertainty and risk are not so clearly distinguishable from each other at the individual
level: indeed, knowledge about the probability distribution of possible outcomes of a choice can
lie anywhere on a continuum, from complete ignorance to certainty. Scholars refer to
when the possible outcomes are unknown whereas they
when only a single
outcome is known to result. In the middle of these two extremes two different degrees of partial
ignorance can be found being uncertainty and risk (Weber & Johnson, 2008). Modeling depends
strongly on a precise definition of risk and uncertainty in a specific setting: this is why many
different conceptual approaches can be mentioned.
2.2 Modeling decision making under risk
It is possible to identify an evolution in modelling decision making under risk at the individual
level. Over time, scholars in social sciences (including finance scholars, economists, and
psychologists) presented four different models based on a different operationalization of value:
Expected Value Theory, Expected Utility Theory, Risk-return models, and Prospect Theory (see
table one)
Expected Value Theory dates back to the seventeenth century and assumes as the key decision
criterion for individuals the maximization of expected monetary value (EV) of a gamble (X).
According to this theory, individuals should face risky situation only by looking at the objective
expected outcome probability p(x) and decide accordingly. Mathematician Daniel Bernoulli has
been the first to critically address this theory by presenting the paradox of the so-called St.
Petersburg lottery (Bernoulli, 1738): in essence, when confronted with a lottery yielding an infinite
expected value, individuals show a very low willingness to pay a price for playing it. This is due to
the fact that Expected Value Theory does not take into account the subjective benefits an
individual receives from the lottery and, as a consequence, the subjective determination of an
T
xpected value, Bernoulli proposed a
, with u(x) being the utility funcion.
3
Table 1
Four models of decision making under risk
Model
Authors
Expected Value Theory
Huygens (1657)
Expected Utility Theory
Bernoulli (1738)
Risk-Return Models
Markovitz (1954)
Prospect Theory
Kahneman & Twersky (1979)
Formula
V(x) =
In utility theory, the marginal value of money and wealth connected with an opportunity
(including a gamble) is diminishing, ultimately explaining the discrepancy between the objective
expected value and observed behavior. The degree of concavity of the relationship between value
and utility serv
V
N
Morgenstern, 1947). In more recent years, expected utility maximization has been empirically
tested in its reliability to predict decision making in different context, including risky scenarios:
even if results provided mixed evidences, this theory remains a cornerstone in economics (Weber
& Johnson, 2008).
Another approach to overcome the limitations of the Expected Value Theory has been proposed in
finance by Markovitz (1954) with Risk Return models. In every risky opportunity there is a tradeoff
between a given level of risk R(X) and its connected return V(X): both these elements ultimately
X . The assumption underlining this
model is that individuals, for a given level of return, try to minimize their risk. Model parameter b
is an individual index of risk aversion, describing the nature of the tradeoff between the
maximization of return and minimization of risk. In risk-return models, risky options
values are proxied by returns V(X) and different utility functions imply different functional forms
for risk, R(X) (Jia and Dyer, 1997).
All the three economic models presented so far lie on a critical assumption: the risk/return of
choice options and the utility of decision outcomes are determined entirely by the objective value
of possible outcomes and the final wealth generated (Weber & Johnson, 2008). In other words,
possible outcomes are assumed to be evaluated independently from a reference outcome. This is
a strong assumption: whenever possible,
outcome with the counterfactual outcome, that is what they could have selected (Landman,
4
1993). According to regret theory, decision makers strongly fear the situation where the
counterfactual outcome is better than their selected choice: this pushes them to maximize their
expected utility whilst minimizing the possible post-decisional feeling of regret (Loomes & Sudgen,
1982; Bell, 1982). The assumption of independency of reference outcomes (or counterfactual) has
been empirically tested: it has been found that even though both Expected Utility Theory and
Risk Return models possess evident prescriptive and normative strengths, in both cases low
predictive power has been obtained when trying to explain human behavior in risk choice. These
two models are unfortunately far to be descriptive models for decisions under risk and uncertainty
(Ellsberg, 1961; McFadden, 1999; Camerer, 2000; Weber & Johnson, 2008).
Conceptually, prospect theory represents a step forward in decision making research. This theory
assumes that individuals rely on a variety of relative comparisons when evaluating an outcome
(Kahneman & Twersky, 1979; Kahneman, 2003). Consistent with the previous models, Prospect
Theory maintains the decreasing marginal sensitivity assumption, where changes in outcomes
have a decreasing impact on the decision maker the more is gained or lost.
In p
T
er value function (presented in table 1), parameter is measuring the
Indeed a significant part of the systematical deviations from
expected behavior by decision makers can be explained by prospect theory through the value
It is noteworthy to review here two empirical
properties of the value function V(X). First, the value function is concave only in the domain of
gains as individuals become more risk averse as gain increases; on the contrary, the value function
is convex only in the domain of losses as individuals become more risk seeking the greater the size
of their loss. Second, the value function V(X) presents an asymmetry in steepness: in the gain
domain the function is much less steep than in the loss domain, reflecting a greater sensitivity of
decision makers for losses than for gains. Scholars defined the ratio between these two different
fundamental in explaining human choice behavior, from the endowment effect (Thaler, 1980) to
the status quo bias (Samuelson and Zeckhauser, 1988).
Prospect theory presents at least two important considerations that are useful for the purpose of
this paper and will be further developed in the next section. First, it suggests a transformation of
objective probabilities into subjective decision weights as individuals are found to be evaluating
small probability events more likely that they should be if based on their objective occurrence: this
is particularly true for entrepreneurship; second, this theory suggests that decision makers are
more likely to use bias and heuristics (including selection of information bias) to simplify their
process of decision (Busenitz & Barney, 1997). All these elements are connected with
psychological variables that are still not perfectly understood and very hard to be accounted for in
an economic model (Smith, 2003). Nonetheless, they help explaining systematical deviations from
the expected behavior in many empirical settings.
5
2.3 Modeling decision making under uncertainty: the central role of perception in individuals
Very often individuals have to make choices under conditions of uncertainty and their primary
resource to deal with it is their subjective ability to perceive the odds of different possible
outcomes. This is why when modeling decision making under uncertainty, the role of perception
becomes fundamental and research in cognitive psychology has been historically the principal
driver of investigation compared to economics. Three ways of modeling decision making under
uncertainty are presented here: generalized risk return model (Sarin & Weber, 1992), ideal point
model (Lopes, 1987; Weber & Kirsner, 1997), and process tracing methods (Weber & Johnson,
2008).
One of the conceptual approaches used to cope with the absence of objective probability has
been to adapt the existing model of financial risk-return. In order to measure the influence of
subjective perception of risk on determining willingness to pay (WTP) for a risky option X, the
three variables of risk, return and the tradeoff parameter b had to be transformed into
psychological variables (Sarin and Weber, 1992). These transformed variables, unlike in financial
risk return models, can differ due to individual or situational characteristics. Consistently with
such transformation, empirical evidence shows that the same
outcome
can be perceived in systematically different ways by different individuals and cultures (Brachinger
and Weber, 1997). Following these modifications, some scholars in psychology also suggested
another review of the risk return models. Instead of assuming decision makers as subjects that
strive to minimize risk, they argued instead that peo
differ, either as a personality difference (Lopes, 1987) or as a situational difference (Weber and
Kirsner, 1997).
Ideal point models dig more into this issue: the central assumption of these models is that a
decision maker will perceive the riskiness of an alternative as the discrepancy between the
In this
respect, an individual with a lower ideal point would perceive a higher level of risk in an
opportunity with a high objective level of uncertainty than an individual with a higher ideal point.
I
uncertainty, Zuckerman (1979) introduced the construct of sensation-seeking which has its basis in
biology and varies with age and gender (Zuckerman et al., 1988). Empirical research suggest two
important evidences: first, the existence of a positive link between sensation-seeking and risk
taking behavior in a variety of settings, from ethical risk to thrill seeking (Weber Et Al, 2002) and
second, the role of risk perception in influencing such link, creating biases when evaluating
opportunities.
Another approach in cognitive psychology that has addressed the problem of modeling decision
making under uncertainty is process analysis. The basic idea behind this approach is to
complement existing risk models considering more sources of data that could help understand the
6
subjective dynamics of decision making. Three examples of process-analysis techniques are
presented here: first, talk aloud techniques, where individuals explain their thoughts as they make
risky choices (Ericsson and Simon, 1980; Bettman and Park, 1980), second, eye fixation/tracking
techniques, useful to gather information on visually displayed information while individuals make
choices (Russo and Dosher, 1983; Bradley Et Al, 2008); finally, computer-based techniques,
recording the mouse clicks that reveal information on the computer screen (Payne Et Al, 1991;
Costa-Gomes Et Al, 2001; Gabaix Et Al, 2006).
The empirical testing of these techniques has revealed several interesting patterns in risky
decision making. First, compared to simple binary lotteries, complex gambles entail a different
kind of decision processing: more specifically, decision-makers try to eliminate options and attend
to only a subset of the available information when faced with complex displays or time pressure
(Weber & Johnson, 2008). The implication is that scholars should carefully choose between
gambles structures when assessing risky choice
different according to the different experimental environments they will face. Second, different
choice processes can be observed as a result of different ways of measuring preferences. The
behavioral inconsistencies between gamble selection and gamble pricing provides an example to
support such statement: it is not unusual to observe that individuals will price the gamble they
select lower than the alternative unchosen gamble. This inconsistency, known as reversal
(Lichtenstein and Slovic, 1971), occurs because decision makers use different processes and put
different weight on probabilities and payoffs when generating a price than when making a choice
(Schkade & Johnson, 1989; Mellers Et Al, 1992). Finally, there is significant evidence of shifts in
strategies across different kinds of problems especially when variables such as the time available
to make a decision or the nature of the choice set changes (Ben Zur & Breznitz, 1981; Payne Et Al,
1988).
3. METHODOLOGICAL REFLECTIONS ON RISK PERCEPTION
The conceptual review of risk and uncertainty provides strong elements to support a further
investigation of risk perception role in decision making: risk perception is indeed believed to be
the key variable to understand inconsistent behaviors. In this respect, entrepreneurship provides a
perfect setting as decision makers have to face high level of risk and uncertainty on a daily basis.
Despite empirical research has already provided some support on the importance of risk
perception in explaining differences between entrepreneurs and managers (Brockhaus, 1982;
Cooper Et Al, 1988), both the conceptualization and, most importantly, operationalization of risk
perception in entrepreneurship remains a matter of debate.
The entrepreneurship research dealing with
risk perception clearly points at the
urgency of addressing two distinct problems: first the confusion over the role of risk perception in
7
influencing entrepreneurial behavior, and second the lack of agreement on how to measure such
perception.
The definition and role of risk perception vs risk attitude
Many studies in psychology and social science include both the constructs of risk perception and
risk attitude in explaining entrepreneurial behavior but systematic analysis of their separate
influences on choice making is a relatively recent achievement in management literature. These
constructs are conceptually distinct but interlinked at the same time (Sitkin & Pablo, 1992; Sitkin &
Weingart, 1995). Therefore, as this linkage often creates confusion, it is important here to
consider both their definitions and then provide a review of the research gaps underlying the role
of risk perception. Definition-wise, risk perception and risk attitude are different and should not be
confused.
Perceived risk is defined instead as the subjective judgment of the amount of risk inherent in a
situation (Keh Et Al, 2002). Since the early work of Palich & Bagby (1995), risk perception has been
investigated as the key characteristic that differentiates entrepreneurs from managers in their
behavior. The two authors found that, ceteris paribus, entrepreneurs tend to perceive some
business situations more positively than do non-entrepreneurs. It is this different perception that
let them see strengths and opportunities where others see weaknesses and threats, positively
influencing venture formation. It follows that cognition plays a big role in the perception of
opportunities: but this connection can also be negative, as previous empirical research found that
probabilities connected with an economic return that are objectively very small can be perceived
as substantially higher due to cognitive biases (Barney & Busenitz, 1997). Later work by Forlani &
Mullins (2000) confirmed and built on such results, finding no significant influence of risk attitude
on risk perception, and ultimately suggesting a strong direct effect of perception alone in decision
making.
Despite their differences, all these studies share a similar approach in underlining the difficulties of
finding a generally accepted role of risk perception, particularly when looking at its antecedents
(Keh Et Al, 2002). Limited research has explored the influence of cognitive factors on risk
perception and mixed empirical evidence has been found to support it. (Simon Et. Al., 1999; Keh
Et. AL. 2002). Further research seems to be hindered by the endogenous low predictive power of
methodologies based on questionnaires (Stewart & Roth, 2001). Therefore, the role of risk
perception remain somehow vague and its definition in entrepreneurship literature, ultimately,
incomplete.
Risk is generally recognized to be tightly linked with the objective variability of outcomes, where
greater variability in economic returns means greater risk. Following these characteristics, risk
attitude has been defined as the stable
used both in the expected utility theory and risk return models to capture differences in choice
behavior through a parameter describing the curvature of the utility function (Weber & Johnson,
2008). Scholars disagree on the definition of risk attitude as a personality trait: some authors have
8
considered risk attitude as a facet of the trait of extraversion, consistent with the Big Five
personality theory (Weber & Milliman, 1997; Zhao & Seibert, 2006), but some others have
questioned such hypothesis (MacCrimmon & Wehrung, 1990; Hanoch Et Al, 2006).
According to Stewart & Roth (2001), empirical research exploring differences between
has produced conflicting results because of the same
two orders of problems: the lack of a precise definition of risk attitude, and a rigorous
measurement technique. The two authors found that differences between entrepreneurs and
managers strongly depend on the definition of entrepreneurial risk attitude, and criticize how
scholars have often overlooked the distinction between entrepreneurs who primarily focus on
growth and profit, and entrepreneurs who focus on producing a family income as small business
owners (Carland et al, 1984). As these individuals have different goals and different risk attitudes,
it is suggested that empirical results measuring risk attitude have been inconclusive partly due to
the different reasons of entrepreneurial self-selection (ibid).
3.1 Risk perception measurement
By reviewing the most recent papers in entrepreneurship research, it is possible to identify two
types of methodological designs capturing risk perception: scenario-based questionnaires and
money games.
Measuring risk perception through scenario based questionnaires
In scenario based questionnaires individuals are asked to answer questions based on one or more
hypothetical scenarios. Many different disciplines in social science (including psychology and
entrepreneurship) have used such design extensively to capture the complexity of decision making
(Stewart & Roth, 2001). Thanks to its flexibility in design, scenario based questionnaires can have
many different structures and measure different cognition related variables including risk
perception. When dealing with such design, it is important to control for different types of bias:
among others we cite here sample and measurement bias. The presence of sample biases
constitutes a well-known problem when making inferences in risk perception. Some studies have
used small sample sizes (Sarasvathy Et. Al., 1998) and external validity might be questioned due to
possible biases in sampling. A
in the sample of scrutiny (Forlani & Mullins, 2000). This is particularly interesting
as some scholars believe that serial entrepreneurs, defined as entrepreneurs that founded more
than one firm, can be less affected by the cognitive biases as they might better assess sunk costs,
knowing when to persist or to give up their effort (Baron & Markman, 2003). If not accounted for,
this evidence might decrease the predictive power of empirical analysis on risk perception.
Furthermore, most of the studies do not provide information on the gender characteristics in the
9
sample. In some cases, the percentage of females in the sample is very low (only 3% of females in
sample for Keh Et Al, 2002) and, even though this evidence might be in line with the consideration
that female entrepreneurship is less common than male entrepreneurship, this again limits the
generalization of results.
Issues related with measurement biases have also exacerbated the discord concerning
entrepreneurial risk perception. Due to the very different nature of the variables to measure,
there is here a fundamental difference that can be found in the approaches to operationalize risk:
whilst scholars exploring risk attitude have often derived empirical measures of absolute/relative
risk attitude from utility theory, scholars exploring risk perception have used an approach based
on revealed perception in questionnaires. In entrepreneurial risk attitude research it is possible to
find a generally accepted measure of absolute risk attitude that is the Arrow-Pratt measure [ARA
u(x) =
] defined as the ratio between the (negative) second and the (positive) first
derivatives
Pratt, 1964; Cramer Et Al, 2002). In risk perception,
such generally accepted measure does not exist (March & Shapira, 1987; Stewart & Roth, 2001).
Table two list the most used methodologies in extant entrepreneurship research to make
inferences on risk perception. Two main instruments are found to be more used in the literature
but none of them is considered superior in predictive power than other (Stewart & Roth, 2001).
On the one hand, the Kogan-Wallach Choice Dilemmas Questionnaire (CDQ) contains twelve
scenarios that describe a person faced with a choice of pursuing risky choice with high return or
pursuing a less risky action with a lower return (Kogan & Wallach, 1964). Respondents are also
asked in either case to indicate the minimum probability of success that would be sufficient to
warrant the choice of the risky alternative, indirectly assessing their perception of the riskiness of
the alternative. The main limitation and critique of such instrument is that it relies on projected
assessments of risk instead of actual behavior on tasks (Shaver & Scott, 1991; Stewart & Roth,
2001) and that in general CDQ has showed little low reliability in predicting covariance between
entrepreneurial behavior and risk.
On the other hand, the Jackson Personality Inventory (JPI) is a 16 personality variables index with a
risk taking scale designed to assess the willingness to commit to either successful or unsuccessful
decisions knowing the corresponding outcomes. Compared to CDQ, this instrument face
individuals with tasks designed to trigger responses under various types of risks including social,
physical, ethical, and financial risks.
The other three instruments presented here can be considered as adaptations of the two previous
mentioned ones, as they present elements from both CDQ and JPI. Overtime, these other three
instruments have focused on a more precise operationalization of risk perception. The Risk
Propensity Scale measure the level of risk propensity through a four items 7-point Likert scale
Palich & Bagby, 1995). Reliability of the scale
C
analysis to check for its unidimensionality
through the various items.
10
Table 2
Methodologies used to capture risk perception through scenario based questionnaires
Instrument
Authors
Results
CDQ (Choice Dilemmas Questionnaire)
Brockhaus (1980)
Differences between
entrepreneurs and managers
in their risk propensity are
driven by risk perception.
Entrepreneurship major
students had higher risk
propensity than non-business
majors.
Compared to small business
managers, founders had higher
risk propensity
Entrepreneurs had higher risk
propensity than managers
No difference in risk
propensity is found between
entrepreneurs and nonentrepreneurs. Risk perception
is, however, a differential
element between the two
Entrepreneurs may not
perceive risk because of the
influence of cognitive biases
Entrepreneurial behavior is
influenced by cognitive biases
through the mediating effect
of risk perception
Sexton & Bowman (1983)
JPI (Jackson Personality Inventory)
Begley & Boyd (1987)
Stewart Et Al (1999)
RPS (Risk Propensity Scale)
Palich & Bagby (1995)
8-items closed-ended questions
Simon Et Al (2000)
RSS (Risk Style Scale)
Forlani & Mullins (2000); Keh
Et. Al. (2002)
A questionnaire design that is in between CDQ and JPI is the 8-items closed-ended question scale
used by Simon Et Al (2000). Here the authors provide a twelve page school case and then measure
risk perception by asking questions on their agreement (disagreement) on statements that deal
with risk. A Likert-style scale and factor analysis is used for robustness. Compared with previous
studies, the authors specifically aimed at measuring the impact of risk perception directly on
entrepreneurial behavior.
Finally, the RSS scale focuses specifically on the perception of monetary risk: the instrument
perception of risk involved in each case. The focus on risk propensity using monetary values has
been indicated as the biggest advantage of this type of scenario based questionnaire (Forlani &
Mullins, 2000; Keh Et Al, 2002).
11
Advantages and disadvantages of scenario based questionnaires
The main advantages of using questionnaires in experimental settings consist in their relative ease
to use, limited costs connected with the implementation of the experiments and the flexibility of
design they allow for. Questionnaires can include longer or shorter cases depending on the level of
complexity that experimenters want to obtain. However all these different forms of
questionnaires have in common many issues that are hard to control for and that have limited the
applicability of such research designs in entrepreneurship (Stewart & Roth, 2001). The first
common problem pertains to the tradeoff between using open or closed questions: in the first
case, it is of fundamental importance to know if the respondent understands the questions being
asked while in the second case the increased level of precision is obtained at the cost of obtaining
minimal, too narrow information (Saccuzzo, 2012). The second common problem pertains to the
inability of questionnaires to measure antecedents of risk perception as they record perception
after the individual has made a decision; in this sense, questionnaires are
es.
Scholars have therefore all suggested to further investigate the decision making process
that is by looking specifically at entrepreneurial perception before decisions. Finally, some authors
underlines the problem of risk measurement when dealing with irrational agents like
entrepreneurs: both risk perception and attitude are influenced by prospects (Kahneman &
Tversky, 1979; Cramer Et Al, 2002) and diversity of case studies used in instruments in terms of
details provided might lead entrepreneurs to behave in a different way than they would on a real
life scenario.
Measuring risk perception through money games
In the case of money games, the experiments include the use of real stakes in order to trigger
entrepreneurial behavior in individuals facing risk and uncertainty. The focal point in money games
is expressed behavior: researchers
choices (and perception) by reducing the
complexity typical of case-based designs. The usual stake involved in such a design is monetary
outcomes: individuals participating in money games typically receive a fixed participation sum and
a variable sum depending on their performance during the experiment (Weber & Milliman, 1997).
Using money should work as an incentive for decision makers to perform in a focused and
strategic way as decisions ultimately influence a real monetary outcome. Even though this
assumption is often empirically questioned in entrepreneurship (see Weber Et Al, 2002) it is
strongly theoretically supported by the widely known axiom of non-satiation, which posit that
individuals always prefer a higher monetary outcome whenever possible. Weber & Milliman
(1997) adopted money games to explore behavior in a dynamic decision environment represented
by different risky investments. The sample of MBA students, after receiving an initial endowment,
was asked to choose between six different risky investments in multiple rounds. Information and
payouts were widely available to participants during the experiment. Results showed the central
12
role of risk perception in driving behavior in such a dynamic environment and the stability of risk
attitudes. Authors suggest that the differences in risky choices by individuals should not
s for risk, but may
also, at least partially, be the result of changes in their perception of the risks due to outcome
feedback. A different paper in neuroscience by Huettel Et Al (2006) also used money games to
investigate risk perception in individuals. Authors asked subjects to make decisions between two
alternatives. The composition of such alternatives was random and contained three different
types of gambles: certain, risky and ambiguous.
In this study, parameters for subjects risk and ambiguity preferences were estimated with their
history of choices in both functional magnetic resonance (fMRI) scanning and classic behavioral
sessions. Results suggested that the mechanisms that support risk and uncertainty perceptions in
humans are separate, as different areas of the brain activated during decision making. If these
results would be confirmed with a sample of entrepreneurs, it would be of great importance to
test which areas of the brain are activated during decision making and how these activations are
different from non-entrepreneurs.
Advantages and disadvantages of money games
Money games possess great advantages when it comes to operationalize variables that deal with
risk attitude and risk perception. Risk-return models and expected utility theory need an objective
assessment of
willingness to pay and utility respectively and both are based on the
assumption that individuals indicate realistic choices in experiments. The presence of real payouts
increases the level of reliability of choices that subjects make and help explaining their real
behavior as a result of two mechanisms. Focus on tasks is increased due the performance effect
on payoffs (De Martino Et Al, 2006) and through a better understanding of the tasks to be
performed by individuals. Money games usually have a simple structure with gambles that are
easily understandable in order to minimize the effects of complexity on decision making and
maximize the role of behavior. The use of neuroscience tools (such as fMRI) can strongly help the
internal and external validity of behavioral results obtained from money games. This is due to the
additional information on
explanation of individuals
M
E A
). For example, research in neuroscience
has confirmed the hypothesis that risky choices are a result of a dual-processing system in the
brain, where risk and uncertainty perception are functions of separate activities in brain areas
(Huettel Et Al, 2006)
The advantages of using money games are usually counterbalanced by disadvantages in
entrepreneurship and this has strongly limited their use. Possibly the most important disadvantage
of using money games is the hard-to-explain theoretical linkage between behavior observed and
the subjective payoff levels that scholars use. In practical terms this means that it is hard to
support theoretically a strong generalization of results coming from money games. A concrete risk
13
when dealing with money games is the possibility of choosing a wrong level (usually too low) of
payoffs for the sample investigated. This is possible not only when monetary resources are scarce
for working on a large enough sample, but also when trying to assess the monetary incentives
needed for making entrepreneurs participate and behave naturally in an experimental setting.
Entrepreneurs often find themselves evaluating opportunities based on the relative importance of
investment needed to fund the venture, the variability in the expected anticipated outcomes, and
any potential loss which may entail such investment (Forlani & Mullins, 2000). Even though all
these elements are usually impossible to account for when evaluating an opportunity, they all
influence risk perception. For these reasons, low levels of stakes involved in money games make
fluence in
perception nor the complexity of entrepreneurial behavior (March & Shapira, 1987). For example,
in some cases, despite games contained decisions on substantial endowments (e.g. 100k dollars),
the real mean payoffs that individuals could obtain due to these decisions were almost irrelevant
(17 dollars was the average payoff in Weber & Milliman, 1997). As expected, this is due to the
tradeoff between working with a large sample and low payoffs, and working with a relatively small
sample with more realistic payoffs (Simon Et. Al., 2000). As a further evidence of the care scholars
need to use when generalizing results coming from money games, Weber et al . (2002) found that
the decisions adopted for monetary gambling reflected real-world investment decisions far worse
than assessed risk-taking for investment decisions, even though both were about monetary
returns (Weber & Johnson, 2008).
3.2 Risk perception measurement in Entrepreneurship: hybrids
There is a tension in entrepreneurship research between money games and scenario based
questionnaires. Which one of the two is best suited to capture risk perception? Or when should
we use one or the other? On the one hand, complexity is always present in entrepreneurial
decision making (Busenitz & Barney, 1997). Furthermore, money incentives alone cannot explain
entrepreneurial behavior through the moderating effect of risk perception (Simon Et Al, 2000;
Weber & Johnson, 2008). Both these reasons have provided support for the use of scenario based
questionnaires in entrepreneurship. On the other hand, scenario based questionnaires have been
plagued by biases and low predictive power in explaining risk perception (Stewart & Roth, 2001).
Behavior on task is more easily captured by money games than scenario based questionnaires.
To solve this tension, scholars in entrepreneurship started using a mixed approach between
money games and scenario based questionnaires. We propose here a new categorization of these
games, calling them hybrids.
A hybrid design refers to the inclusion of money games in scenario-based questionnaire designs
but without the presence of any real monetary payoff for individuals. The focus of studies using
such a methodology is to address the tradeoff between an acceptable level of complexity in the
14
Table
Methodologies used to capture risk perception through money games and hybrids.
3
Instrument
Authors
Results
Money games (with payouts)
Weber & Milliman (1997)
Choices in a dynamic decision
environment that involves
repeated decisions and
continuous outcome feedback
can be attributed to changes in
risk perception.
The mechanisms that support
risk and uncertainty
perceptions in humans are
separate and due to different
areas of the brain activated
during decision making.
Entrepreneurship is
discouraged by the individual
degree of risk aversion as data
suggests that entrepreneurs
are less risk averse than nonentrepreneurs.
Behavioral results indicate that
subjects' decisions are affected
by framing manipulation of
gambles, with subjects being
risk-averse in the gain frame
and risk-seeking in the loss
frame
Huettel Et Al (2006)
Hybrids (no payouts)
Cramer Et Al (2002)
De Martino Et Al (2006)
scenarios presented during the experiments and a more objective evaluation of behavior when
individuals face monetary risky choices. With a hybrid design scholars aim to capture a more
realistic behavior of individuals, overcoming the difficulties of the other two methods (e.g. a large
sample can be used as individuals do not need to be paid, questions are hardly misunderstood
with money games). Table three presents a comparison between money games and hybrids based
on payoffs differences. Cramer Et Al (2002) used a hybrid methodology finding a negative impact
-selection in entrepreneurship. Methodologically, the authors
derived from utility theory an empirical measure of absolute and relative risk aversion by asking
individuals their reservation price (WTP in risk return models) when facing a lottery. In
neuroscience, De Martino Et Al (2006) provided a further empirical validation of prospect theory:
after receiving an initial endowment, participants were asked to choose between a "sure" and a
"gamble" option (with equal expected value) presented in the context of two different frames
15
(Gain frame or Loss frame). In this case, the behavior of subjects was the relevant variable to
measure and therefore only pie charts for visualization ease were provided instead of a case.
Being somewhere in between two designs, hybrids possess some of the advantages and some of
the disadvantages of the two. Therefore, when compared to money games and questionnaires, it
cannot be considered as a superior design. In general, what makes a hybrid a very useful (and
differential) design is the possibility of addressing two different types of research aim (exploring
complexity in decision making and observed behavior in risky gambles) keeping the flexibility and
lower costs of a questionnaire. The main disadvantage relies in the difficulties for experimenters
to cope with the drawbacks of having a money game structure with no payoffs and a
questionnaire structure with complex questions compared to gambles.
4. CONCLUSIONS: HYBRIDS FOR FUTURE ENTREPRENEURSHIP RESEARCH
The first purpose of this paper has been to review methodologies used to trigger entrepreneurial
behavior in a variety of settings involving risk and uncertainty. Even though risk can be modelled
perception the variable that plays
a key role in determining behavior in different risky situations.
Figure 1
Entrepreneurial behavior research through risk perception: a design map (read from top to bottom)
16
In entrepreneurship, individuals have to face risk and uncertainty on a daily basis in their decision
making and often rely on bias and heuristics (Busenitz & Barney, 1997): risk models had to be
adapted in order to measure behavior in a methodologically accurate way. In this respect we have
identified three types of methodological designs: money games, scenario based questionnaires,
and hybrids.
The second purpose of this paper has been to suggest the use of games in specific settings. After
looking at their advantages and disadvantages, it is suggested that the use of either questionnaires
or money games to measure risk perception would be dependent on different aims: behavior on
tasks or complexity (see figure one). Hybrids would suit best the context of entrepreneurship as
both the research aims co-exist in entrepreneurial decision making. On the contrary, in psychology
and economics risk perception can be captured by looking at complexity or behavior on tasks only.
We suggest that the use of hybrids has to be carefully managed, especially when designing the
games. Sample selection (students or entrepreneurs) and the research question focus (behavior or
complexity) have to drive the balance between the scenario length and the money games
structure included in the hybrid.
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